Regarding "equivalent models" (Muthen, 2003) and testing model assumptions... 1. Is it necessary to check for outliers while using GMM, given that extreme values could be the defining characteristic of classes (for example, do I still need to look at tech13 for Mardia's stat)? 2. The y's making up my classes are normal, covariates are correlated, distal variables are negatively skewed & highly correlated (.4-.6). What should I be concerned with in this scenario?
Defining Class Indicators: I get different results depending on whether I use a single (avg) class indicator or use the same individual items as separate indicators. 1. Avg score masks individual item contribution, but what might be the advantage to using the average?
Step-wise vs. Simultaneous (Full Model) Approach: How does one specify MULTIPLE CONTINOUS distal outcomes (and their correlation) while simultaneously using covariates in a GMM? The only syntax I can find uses the savedata option to later test c on u or c on x, but these are never simultaneously specified.
Inspection and Specification of Residual Variances: IF everything else is equal, can separate models with different class-specific residual constraints be compared or are interpretations of each model different?